A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
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Updated
Apr 29, 2025 - Python
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
Pocket Flow Tutorial Project: Turns GitHub repo into Easy Tutorial with AI
Pocket Flow: 100-line LLM framework. Let Agents build Agents!
Harness LLMs with Multi-Agent Programming
No-code multi-agent framework to build LLM Agents, workflows and applications with your data
[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
[GenAI Application Development Framework] 🚀 Build GenAI application quick and easy 💬 Easy to interact with GenAI agent in code using structure data and chained-calls syntax 🧩 Use Agently Workflow to manage complex GenAI working logic 🔀 Switch to any model without rewrite application code
Official Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.
Your 24/7 On-Call AI Agent - Solve Alerts Faster with Automatic Correlations, Investigations, and More
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured output. Works also with models not fine-tuned to JSON output and function calls.
InternEvo is an open-sourced lightweight training framework aims to support model pre-training without the need for extensive dependencies.
Super-Efficient RLHF Training of LLMs with Parameter Reallocation
FineTune LLMs in few lines of code (Text2Text, Text2Speech, Speech2Text)
Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs.
A ReAct-Based Highly Robust Autonomous Agent Framework
AI-to-AI Testing | Simulation framework for LLM-based applications
Simplify interactions with Large Language Models
The Library for LLM-based multi-agent applications
An Execution Isolation Architecture for LLM-Based Agentic Systems
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